263 lines
7.9 KiB
R
263 lines
7.9 KiB
R
# BIN-SEQA-Composition.R
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-SEQA-Comparison unit
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#
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# Version: 1.0
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#
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# Date: 2017 11 17
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# V 1.0 First live version 2017
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# V 0.1 First code copied from BCH441_A03_makeYFOlist.R
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#
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# TODO:
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#
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#
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# == HOW TO WORK WITH LEARNING UNIT FILES ======================================
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#
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# DO NOT SIMPLY source() THESE FILES!
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# If there are portions you don't understand, use R's help system, Google for an
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# answer, or ask your instructor. Don't continue if you don't understand what's
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# going on. That's not how it works ...
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#
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# ==============================================================================
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#TOC> ==========================================================================
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#TOC>
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#TOC> Section Title Line
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#TOC> ----------------------------------------------------
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#TOC> 1 Preparation 41
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#TOC> 2 Aggregate properties 63
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#TOC> 3 Sequence Composition Enrichment 106
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#TOC> 3.1 Barplot, and side-by-side barplot 129
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#TOC> 3.2 Plotting ratios 164
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#TOC> 3.3 Plotting log ratios 180
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#TOC> 3.4 Sort by frequency 195
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#TOC> 3.5 Color by amino acid type 210
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#TOC>
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#TOC> ==========================================================================
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# = 1 Preparation =========================================================
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if (!require(seqinr, quietly=TRUE)) {
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install.packages("seqinr")
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library(seqinr)
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}
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# Package information:
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# library(help = seqinr) # basic information
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# browseVignettes("seqinr") # available vignettes
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# data(package = "seqinr") # available datasets
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# Load a reference sequence to work with:
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# If you have done the BIN-Storing_data unit:
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source("makeProteinDB.R")
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sel <- which(myDB$protein$name == sprintf("MBP1_%s", biCode(MYSPE)))
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mySeq <- myDB$protein$sequence[sel]
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# If not, use the yeast Mbp1 sequence:
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mySeq <- dbSanitizeSequence(fromJSON("./data/MBP1_SACCE.json")$sequence)
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# = 2 Aggregate properties ================================================
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# Let's try a simple function from seqinr: computing the pI of the sequence
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?computePI
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# This takes as input a vector of upper-case AA codes
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# We can use the function strsplit() to split the string
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# into single characters
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(s <- strsplit(mySeq, "")) # splitting on the empty spring
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# splits into single characters
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s <- unlist(s) # strsplit() returns a list! Why?
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# (But we don't need a list now...)
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# Alternatively, seqinr provides
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# the function s2c() to convert strings into
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# character vectors (and c2s to convert them back).
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s2c(mySeq)
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computePI(s2c(mySeq)) # isoelectric point
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pmw(s2c(mySeq)) # molecular weight
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AAstat(s2c(mySeq)) # This also plots the distribution of
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# values along the sequence
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# A true Labor of Love has gone into the
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# compilation of the "aaindex" data:
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?aaindex
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data(aaindex) # "attach" the dataset - i.e. make it accessible as an
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# R object
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length(aaindex)
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# Here are all the index descriptions
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for (i in 1:length(aaindex)) {
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cat(paste(i, ": ", aaindex[[i]]$D, "\n", sep=""))
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}
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# = 3 Sequence Composition Enrichment =====================================
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# Lets use one of the indices to calculate and plot amino-acid
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# composition enrichment:
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aaindex[[459]]$D
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#
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# Let's construct an enrichment plot to compare average frequencies
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# with the amino acid counts in our sequence.
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(refData <- aaindex[[459]]$I) # reference frequencies in %
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names(refData) <- a(names(refData)) # change names to single-letter
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# code using seqinr's "a()" function
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sum(refData)
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refData # ... in %
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# tabulate the amino acid counts in mySeq
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(obsData <- table(s2c(mySeq))) # counts
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(obsData <- 100 * (obsData / sum(obsData))) # frequencies
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# == 3.1 Barplot, and side-by-side barplot =================================
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barplot(obsData, col = "#CCCCCC", cex.names = 0.7)
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abline(h = 100/20, col="#BB0000")
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barplot(refData, col = "#BB0000", cex.names = 0.7)
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abline(h = 100/20, col="#555555")
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# Ok: first problem - the values in obsData are in alphabetical order. But the
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# values in refData are in alphabetical order of amino acid name: alanine,
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# arginine, asparagine, aspartic acid ... A, R, N, D, E ... you will see this
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# order a lot - one of the old biochemistry tropes in the field. So we need to
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# re-order one of the vectors to match the other. That's easy though:
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refData
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(refData <- refData[names(obsData)])
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barplot(refData, col = "#BB0000", cex.names = 0.7)
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abline(h = 100/20, col="#555555")
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# To compare the values, we want to see them in a barplot, side-by-side ...
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barplot(rbind(obsData, refData),
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ylim = c(0, 12),
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beside = TRUE,
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col = c("#CCCCCC", "#BB0000"),
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cex.names = 0.7)
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abline(h = 100/20, col="#00000044")
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# ... and add a legend
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legend (x = 1, y = 12,
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legend = c("mySeq", "Average composition"),
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fill = c("#CCCCCC", "#BB0000"),
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cex = 0.7,
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bty = "n")
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# == 3.2 Plotting ratios ===================================================
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# To better compare the values, we'll calculate ratios between
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# obsData and refData
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barplot(obsData / refData,
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col = "#CCCCCC",
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ylab = "Sequence / Average",
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cex.names = 0.7)
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abline(h = 1, col="#BB0000")
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abline(h = c(1/3, 3), lty = 2, col="#BB000055")
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# ... but ratios are not very good here, since the difference in height on the
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# plot now depends on the order we compare in: ratios of 1/3 and 3 (dotted
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# lines) are exactly the same fold-difference !
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# == 3.3 Plotting log ratios ===============================================
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# A better way to display this
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# is to plot log(ratios).
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barplot(log(obsData / refData),
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col = "#CCCCCC",
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ylab = "log(Sequence / Average)",
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cex.names = 0.7)
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abline(h = log(1), col="#BB0000")
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abline(h = log(c(1/3, 3)), lty = 2, col="#BB000055")
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# Note how the three-fold difference lines are now the same distance from the
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# line of equal ratio.
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# == 3.4 Sort by frequency =================================================
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barplot(sort(log(obsData / refData), decreasing = TRUE),
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ylim = c(-3.5,2),
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col = "#CCCCCC",
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ylab = "log(Sequence / Average)",
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cex.names = 0.7)
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abline(h = log(1), col="#BB0000")
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abline(h = log(c(1/3, 3)), lty = 2, col="#BB000055")
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arrows(4, 1.8, 0, 1.8, length = 0.07)
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text(5.5, 1.8, "Enriched", cex = 0.7)
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arrows(20, 1.8, 24, 1.8, length = 0.07)
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text(19.5, 1.8, "Depleted", pos = 2, cex = 0.7)
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# == 3.5 Color by amino acid type ==========================================
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# color the bars by type.
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# define colors
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AAcol <- character()
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AAcol["A"] <- "#AABBAA"
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AAcol["C"] <- "#FFEE77"
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AAcol["D"] <- "#DD6600"
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AAcol["E"] <- "#DD3300"
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AAcol["F"] <- "#767D38"
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AAcol["G"] <- "#BBBBCC"
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AAcol["H"] <- "#A2A1FD"
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AAcol["I"] <- "#70B6C6"
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AAcol["K"] <- "#4563BB"
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AAcol["L"] <- "#80C6B6"
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AAcol["M"] <- "#AFCC34"
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AAcol["N"] <- "#BB88CC"
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AAcol["P"] <- "#7292B7"
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AAcol["Q"] <- "#8866BB"
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AAcol["R"] <- "#74A0FF"
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AAcol["S"] <- "#9999CC"
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AAcol["T"] <- "#99AADD"
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AAcol["V"] <- "#9DB500"
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AAcol["W"] <- "#76AD48"
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AAcol["Y"] <- "#44CA97"
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barplot(rep(1, 20), names.arg = names(AAcol), col = AAcol, cex.names = 0.5)
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lR <- sort(log(obsData / refData), decreasing = TRUE)
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barplot(lR,
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ylim = c(-3.5,2),
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col = AAcol[names(lR)],
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ylab = "log(Sequence / Average)",
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cex.names = 0.7)
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abline(h = log(1), col="#00000055")
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abline(h = log(c(1/3, 3)), lty = 2, col="#00000033")
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arrows(4, 1.8, 0, 1.8, length = 0.07)
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text(5.5, 1.8, "Enriched", cex = 0.7)
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arrows(20, 1.8, 24, 1.8, length = 0.07)
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text(19.5, 1.8, "Depleted", pos = 2, cex = 0.7)
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# Task:
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# Interpret this plot. (Can you?) Which types of amino acids are enriched?
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# Depleted?
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# [END]
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